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neuralel_tadir.py
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neuralel_tadir.py
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import os
import sys
import copy
import json
import pprint
import numpy as np
import tensorflow as tf
from os import listdir
from os.path import isfile, join
from ccg_nlpy.core.view import View
from ccg_nlpy.core.text_annotation import TextAnnotation
from readers.inference_reader import InferenceReader
from readers.test_reader import TestDataReader
from readers.textanno_test_reader import TextAnnoTestReader
from models.figer_model.el_model import ELModel
from readers.config import Config
from readers.vocabloader import VocabLoader
np.set_printoptions(threshold=np.inf)
np.set_printoptions(precision=7)
pp = pprint.PrettyPrinter()
flags = tf.app.flags
flags.DEFINE_integer("max_steps", 32000, "Maximum of iteration [450000]")
flags.DEFINE_integer("pretraining_steps", 32000, "Number of steps to run pretraining")
flags.DEFINE_float("learning_rate", 0.005, "Learning rate of adam optimizer [0.001]")
flags.DEFINE_string("model_path", "", "Path to trained model")
flags.DEFINE_string("dataset", "el-figer", "The name of dataset [ptb]")
flags.DEFINE_string("checkpoint_dir", "/tmp",
"Directory name to save the checkpoints [checkpoints]")
flags.DEFINE_integer("batch_size", 1, "Batch Size for training and testing")
flags.DEFINE_integer("word_embed_dim", 300, "Word Embedding Size")
flags.DEFINE_integer("context_encoded_dim", 100, "Context Encoded Dim")
flags.DEFINE_integer("context_encoder_num_layers", 1, "Num of Layers in context encoder network")
flags.DEFINE_integer("context_encoder_lstmsize", 100, "Size of context encoder hidden layer")
flags.DEFINE_integer("coherence_numlayers", 1, "Number of layers in the Coherence FF")
flags.DEFINE_integer("jointff_numlayers", 1, "Number of layers in the Coherence FF")
flags.DEFINE_integer("num_cand_entities", 30, "Num CrossWikis entity candidates")
flags.DEFINE_float("reg_constant", 0.00, "Regularization constant for NN weight regularization")
flags.DEFINE_float("dropout_keep_prob", 0.6, "Dropout Keep Probability")
flags.DEFINE_float("wordDropoutKeep", 0.6, "Word Dropout Keep Probability")
flags.DEFINE_float("cohDropoutKeep", 0.4, "Coherence Dropout Keep Probability")
flags.DEFINE_boolean("decoder_bool", True, "Decoder bool")
flags.DEFINE_string("mode", 'inference', "Mode to run")
flags.DEFINE_boolean("strict_context", False, "Strict Context exludes mention surface")
flags.DEFINE_boolean("pretrain_wordembed", True, "Use Word2Vec Embeddings")
flags.DEFINE_boolean("coherence", True, "Use Coherence")
flags.DEFINE_boolean("typing", True, "Perform joint typing")
flags.DEFINE_boolean("el", True, "Perform joint typing")
flags.DEFINE_boolean("textcontext", True, "Use text context from LSTM")
flags.DEFINE_boolean("useCNN", False, "Use wiki descp. CNN")
flags.DEFINE_boolean("glove", True, "Use Glove Embeddings")
flags.DEFINE_boolean("entyping", False, "Use Entity Type Prediction")
flags.DEFINE_integer("WDLength", 100, "Length of wiki description")
flags.DEFINE_integer("Fsize", 5, "For CNN filter size")
flags.DEFINE_string("optimizer", 'adam', "Optimizer to use. adagrad, adadelta or adam")
flags.DEFINE_string("config", 'configs/config.ini',
"VocabConfig Filepath")
flags.DEFINE_string("test_out_fp", "", "Write Test Prediction Data")
flags.DEFINE_string("tadirpath", "", "Director containing all the text-annos")
flags.DEFINE_string("taoutdirpath", "", "Director containing all the text-annos")
FLAGS = flags.FLAGS
def FLAGS_check(FLAGS):
if not (FLAGS.textcontext and FLAGS.coherence):
print("*** Local and Document context required ***")
sys.exit(0)
assert os.path.exists(FLAGS.model_path), "Model path doesn't exist."
assert(FLAGS.mode == 'ta'), "Only mode == ta allowed"
def getAllTAFilePaths(FLAGS):
tadir = FLAGS.tadirpath
taoutdirpath = FLAGS.taoutdirpath
onlyfiles = [f for f in listdir(tadir) if isfile(join(tadir, f))]
ta_files = [os.path.join(tadir, fname) for fname in onlyfiles]
output_ta_files = [os.path.join(taoutdirpath, fname) for fname in onlyfiles]
return (ta_files, output_ta_files)
def main(_):
pp.pprint(flags.FLAGS.__flags)
FLAGS_check(FLAGS)
config = Config(FLAGS.config, verbose=False)
vocabloader = VocabLoader(config)
FLAGS.dropout_keep_prob = 1.0
FLAGS.wordDropoutKeep = 1.0
FLAGS.cohDropoutKeep = 1.0
(intput_ta_files, output_ta_files) = getAllTAFilePaths(FLAGS)
print("TOTAL NUMBER OF TAS : {}".format(len(intput_ta_files)))
reader = TextAnnoTestReader(
config=config,
vocabloader=vocabloader,
num_cands=30,
batch_size=FLAGS.batch_size,
strict_context=FLAGS.strict_context,
pretrain_wordembed=FLAGS.pretrain_wordembed,
coherence=FLAGS.coherence,
nerviewname="NER")
model_mode = 'test'
config_proto = tf.ConfigProto()
config_proto.allow_soft_placement = True
config_proto.gpu_options.allow_growth=True
sess = tf.Session(config=config_proto)
with sess.as_default():
model = ELModel(
sess=sess, reader=reader, dataset=FLAGS.dataset,
max_steps=FLAGS.max_steps,
pretrain_max_steps=FLAGS.pretraining_steps,
word_embed_dim=FLAGS.word_embed_dim,
context_encoded_dim=FLAGS.context_encoded_dim,
context_encoder_num_layers=FLAGS.context_encoder_num_layers,
context_encoder_lstmsize=FLAGS.context_encoder_lstmsize,
coherence_numlayers=FLAGS.coherence_numlayers,
jointff_numlayers=FLAGS.jointff_numlayers,
learning_rate=FLAGS.learning_rate,
dropout_keep_prob=FLAGS.dropout_keep_prob,
reg_constant=FLAGS.reg_constant,
checkpoint_dir=FLAGS.checkpoint_dir,
optimizer=FLAGS.optimizer,
mode=model_mode,
strict=FLAGS.strict_context,
pretrain_word_embed=FLAGS.pretrain_wordembed,
typing=FLAGS.typing,
el=FLAGS.el,
coherence=FLAGS.coherence,
textcontext=FLAGS.textcontext,
useCNN=FLAGS.useCNN,
WDLength=FLAGS.WDLength,
Fsize=FLAGS.Fsize,
entyping=FLAGS.entyping)
model.load_ckpt_model(ckptpath=FLAGS.model_path)
print("Total files: {}".format(len(output_ta_files)))
erroneous_files = 0
for in_ta_path, out_ta_path in zip(intput_ta_files, output_ta_files):
# print("Running the inference for : {}".format(in_ta_path))
try:
reader.new_test_file(in_ta_path)
except:
print("Error reading : {}".format(in_ta_path))
erroneous_files += 1
continue
(predTypScNPmat_list,
widIdxs_list,
priorProbs_list,
textProbs_list,
jointProbs_list,
evWTs_list,
pred_TypeSetsList) = model.inference_run()
# model.inference(ckptpath=FLAGS.model_path)
wiki_view = copy.deepcopy(reader.textanno.get_view("NER"))
docta = reader.textanno
el_cons_list = wiki_view.cons_list
numMentionsInference = len(widIdxs_list)
# print("Number of mentions in model: {}".format(len(widIdxs_list)))
# print("Number of NER mention: {}".format(len(el_cons_list)))
assert len(el_cons_list) == numMentionsInference
mentionnum = 0
for ner_cons in el_cons_list:
priorScoreMap = {}
contextScoreMap = {}
jointScoreMap = {}
(wididxs, pps, mps, jps) = (widIdxs_list[mentionnum],
priorProbs_list[mentionnum],
textProbs_list[mentionnum],
jointProbs_list[mentionnum])
maxJointProb = 0.0
maxJointEntity = ""
for (wididx, prp, mp, jp) in zip(wididxs, pps, mps, jps):
wT = reader.widIdx2WikiTitle(wididx)
priorScoreMap[wT] = prp
contextScoreMap[wT] = mp
jointScoreMap[wT] = jp
if jp > maxJointProb:
maxJointProb = jp
maxJointEntity = wT
''' add labels2score map here '''
ner_cons["jointScoreMap"] = jointScoreMap
ner_cons["contextScoreMap"] = contextScoreMap
ner_cons["priorScoreMap"] = priorScoreMap
# add max scoring entity as label
ner_cons["label"] = maxJointEntity
ner_cons["score"] = maxJointProb
mentionnum += 1
wiki_view.view_name = "NEUREL"
docta.view_dictionary["NEUREL"] = wiki_view
docta_json = docta.as_json
json.dump(docta_json, open(out_ta_path, "w"), indent=True)
print("Number of erroneous files: {}".format(erroneous_files))
print("Annotation completed. Program can be exited safely.")
sys.exit()
if __name__ == '__main__':
tf.app.run()